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Contemporary Perspectives in Data Mining

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A volume in
Contemporary Perspectives on Data Mining
Kenneth D. Lawrence and Ronald K. Klimberg, Series Editors

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Contemporary Perspectives in Data Mining

Volume 3

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edited by

Kenneth D. Lawrence

New Jersey Institute of Technology

Ronald K. Klimberg

Saint Joseph’s University

INFORMATION AGE PUBLISHING, INC.
Charlotte, NC • www.infoagepub.com

Library of Congress Cataloging-in-Publication Data

   A CIP record for this book is available from the Library of Congress

      http://www.loc.gov

ISBN: 978-1-64113-054-7 (Paperback)

           978-1-64113-055-4 (Hardcover)

           978-1-64113-056-1 (ebook)

Copyright © 2018 Information Age Publishing Inc.

All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means, electronic, mechanical,
photocopying, microfilming, recording or otherwise, without written permission
from the publisher.

Printed in the United States of America

Contents

Section I

Predictive Analytics

1     Bootstrap Aggregation for Neural Network Forecasting of Supply Chain Order Quantity

Mark T. Leung1 and Shaotao Pan

2     Combining Retrospective and Predictive Analytics for More Robust Decision Support

Thomas Ott and Stephan Kudyba

3     Predictive Analytical Model of the CEO Compensation of Major U.S. Corporate Insurance Companies

Kenneth Lawrence, Gary Kleinman, and Sheila Lawrence

Section II

Business Applications

4     Analyzing Operational and Financial Performance of U.S. Hospitals Using Two-Stage Production Process

Dinesh Pai and Hengameh Hosseini

5     Digital Disruption: How E-Commerce Is Changing the Grocery Game

Will Greerer, Gregory Smith, David Hyland, and Mark Frolick

6     The Hazards of Subgroup Analysis in Randomized Business Experiments and How to Avoid Them

B. D. McCullough

7     Business Intelligence Challenges for Small and Medium-Sized Business: Leveraging Existing Resources

Nick Perrino, Gregory Smith, David Hyland, and Mark Frolick

Section III

Topics in Data Mining

8     Data Mining Techniques Applied to Outcome Analysis and Validation for the Futures Drug and Alcohol Rehabilitation Center

Virginia Miori and Catherine Cardamone

9     An Extended H-Index: A New Method to Evaluate Scientists’ Impact

Feng Yang, Xiya Zu, and Zhimin Huang

10    Why We Need Analytics Grand Rounds

Ronald Klimberg, Richard Pollack, and Richard Herschel

About the Editors

About the Editors

Dr. Kenneth D. Lawrence is a Professor of Management Science and Business Analytics in the School of Management at the New Jersey Institute of Technology. Professor Lawrence’s research is in the areas of applied management science, data mining, forecasting, and multi-criteria decision-making. His current research works include multi-criteria mathematical programming models for productivity analysis, discriminant analysis, portfolio modeling, quantitative finance, and forecasting/data mining. He is a full member of the Graduate Doctoral Faculty of Management at Rutgers, The State University of New Jersey in the Department of Management Science and Information Systems and a Research Fellow in the Center for Supply Chain Management in the Rutgers Business School. His research work has been cited over 1,750 times in over 235 journals, including: Computers and Operations Research, International Journal of Forecasting, Journal of Marketing, Sloan Management Review, Management Science, and Technometrics. He has 375 publications in 28 journals including: European Journal of Operational Research, Computers and Operations Research, Operational Research Quarterly, International Journal of Forecasting and Technometrics. Professor Lawrence is associated editor of the International Journal of Strategic Decision Making (IGI Publishing). He is also associated editor of the Review of Quantitative Finance and Accounting (Springer Verlag), as well as associate editor of the Journal of Statistical Computation and Simulation (Taylor and Francis). He is editor of Advances in Business and Management Forecasting (Emerald Press), editor of Applications of Management Science (Emerald Press), and editor of Advances in Mathematical Programming and Financial Planning (Emerald Press).

Dr. Ronald K. Klimberg, is a professor in the Department of Decision Systems Sciences of the Haub School of Business at Saint Joseph’s University. Dr. Klimberg has published 3 books, including his Fundamentals of Predictive Analytics Using JMP, edited 9 books, over 50 articles and made over 70 presentations at national and international conferences. His current major interest include multiple criteria decision making (MCDM), multiple objective linear programming (MOLP), data envelopment analysis (DEA), facility location, data visualization, data mining, risk analysis, workforce scheduling, and modeling in generation. He is currently a member of INFORMS, DSI, and MCDM. Ron was the 2007 recipients of the Tenglemann Award for his excellence in scholarship, teaching, and research.